利用自监督对比学习从太阳轨道器数据中发现双起源慢速太阳风
Discovering Dual-Origin Slow Wind from Solar Orbiter with Self-Supervised Contrastive Learning
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中文总结 AI 辅助
本研究提出自监督对比深度聚类框架Solar-CDC，利用太阳轨道器数据分离慢速太阳风的不同粒子群体，成功恢复其双起源特征，为日冕源诊断提供新方法。
中文摘要 AI 辅助
慢速太阳风源自单一日冕源还是两个不同通道，仍是日球物理学领域的核心开放问题。解决该问题需要对两组大体速度相近、主要在重离子组成上存在差异的粒子进行无监督分离。我们提出Solar-CDC，这是一种自监督对比深度聚类（CDC）框架，它通过Transformer编码器将等离子体观测值映射到潜在空间，优化三元组间隔损失，并通过k-means更新伪标签。理论上，我们证明t-SNE和UMAP等保留邻域的嵌入方法存在根本局限：保留邻域图会使跨簇割分比例保持不变；保留除ε比例外的所有链接，会使该比例最多变化ε，且两种约束均不依赖目标维度。而间隔目标会重构图并将割分比例推向零。在30602个太阳轨道器观测数据上，30种降维与聚类组合的轮廓系数峰值为0.454，而Solar-CDC达到0.869。仅突破几何约束无法保证物理有效性：TriMap同样优化三元组，达到0.824，但其簇与已发表的组成分类学的匹配度低于随机水平。Solar-CDC则恢复出平均电荷态比为0.080、0.160和0.400的簇，将中间群体置于与日冕洞边界相关的窗口内。即使输入中完全不包含定义性电荷态比，模型仍能恢复基于该电荷态比定义的分类学。因此，Solar-CDC将自监督表征学习与日冕源诊断联系起来，重要的是，仅当由动态更新的物理感知簇而非距离驱动时，学习损失才能恢复物理群体。
英文摘要
Whether the slow solar wind originates from one coronal source or two distinct channels remains a central open question in heliophysics. Resolving this requires unsupervised separation of two populations that arrive at nearly the same bulk speed and differ mainly in heavy-ion composition. We present Solar-CDC, a self-supervised contrastive deep clustering (CDC) framework that maps plasma observables to a latent space via a Transformer encoder, optimizes a triplet margin loss, and updates pseudo-labels via $k$-means. Theoretically, we prove that neighborhood-preserving embeddings such as t-SNE and UMAP are fundamentally constrained. Preserving the neighbor graph leaves the cross-cluster cut fraction unchanged, and preserving all but a fraction $\varepsilon$ of its links moves that fraction by at most $\varepsilon$. Neither bound depends on the target dimension. A margin objective rewrites the graph and drives the cut fraction to zero. Empirically, on 30,602 Solar Orbiter observations, thirty combinations of dimensionality reduction and clustering peak at a silhouette of $0.454$, whereas Solar-CDC reaches $0.869$. Escaping the geometric bound alone does not guarantee physical validity: TriMap also optimizes triplets and reaches $0.824$, yet its clusters score below chance against the published composition taxonomy. Solar-CDC instead recovers clusters with mean charge-state ratios of $0.080$, $0.160$, and $0.400$, placing the intermediate population inside the window associated with coronal-hole boundaries. Even when the defining charge-state ratio is withheld from the inputs entirely, the model still recovers the taxonomy defined on it. Solar-CDC thus connects self-supervised representation learning to coronal source diagnostics. Importantly, a learning loss recovers physical populations only when driven by dynamically updated physically-aware clusters rather than distances.
发表机构
- Baylor University（贝勒大学）
机构由 AI 辅助整理，请以论文原文为准。
